Mobile manipulators, which are robotic systems integrating an automatic or autonomous mobile base with a manipulator, can potentially enhance automation in many industrial and unstructured environments. Namely, large-scale manufacturing processes, typical in the aerospace, energy, transportation, and conformal additive manufacturing fields, encompass a notable subset of potential future mobile manipulator use-cases. Utilizing autonomous mobility for manipulator re-positioning could allow for continuous, simultaneous arm and mobile base cooperation, which is referred to as i.e., continuous performance. Continuous mobile manipulator capabilities may hold particular benefit for large, curved, and complex workpieces. However, such flexibility can also introduce additional sources of performance uncertainty, preventing mobile manipulators from satisfying stringent pose repeatability and accuracy requirements. To identify and quantify this uncertainty, the Configurable Mobile Manipulator Apparatus was developed by the National Institute of Standards and Technology. Previous test implementations with the apparatus included non-continuous mobile manipulator performance, such as static and indexed performance, but continuous performance measurement had only been previously demonstrated in simulation and on proof-of-concept hardware. This dataset was obtained through the transfer of simulations and algorithms for continuous registration to an industrial mobile manipulator platform and through a subsequent 2^3 factorial designed experiment to compare the performance and robustness of two continuous localization methods: 1) A deterministic spiral search and 2) A stochastic Unscented Kalman Filter (UKF) search across two selected mobile base speeds and sides of the CMMA. Supplementary data obtained prior to the experiment, such as source code, calibration data, mobile base map and configuration data, coordinate system measurements, and robot/client to ground-truth system time synchronization is also included, along with the analysis source code and results files generated in conducting the performance evaluation.

Data represent the performance of prototype cooperative automated driving system applications for improving traffic mobility. The applications include the integrated highway prototype that consists of vehicle platooning, speed harmonization, and automated lane change and merge.

,Wyniki badań przemysłowych i prac rozwojowych projektu POIR.01.01.01-00-0630/17-00 : Inteligentny system pomiarów przepływu i masy materiałów sypkich w procesach produkcyjnych oparty na technologii IoT, z wykorzystaniem wagi nowej generacji. Opisano wyniki badań koncepcji nowej wagi do pomiaru przepływu materiałów sypkich oraz inteligentnej sieci pomiarowej wag. udowodniono słuszność koncepcji. Na podstawie osiągniętych wyników zbudowano prototypy urządzeń i poddano je badaniom. Produkty są przygotowane do pracy w inteligentnych sieciach pomiarowych i rozbudowanych systemach produkcyjnych SCADA i MES. Dzięki wdrożeniu nowej technologii użytkownik może zainstalować produkt w miejscach w których dotychczas było to niemożliwe, procedura instalacji została uproszczona, wymiary i ciężar znacząco zredukowane, a obsługa, z uwagi na brak części ruchomych, zredukowana do minimum.,,,

- 상수도관로의 이상을 손상 없이 탐지하기 위해, 소프트 스킨의 생체모방 주행 로봇을 내부 투입하고, 각종 센서 및 장비를 통한 영상/음향 정보를 수집 후, AI 기반 빅데이터 분석 통해 이상유무 판단 및 예측 수행할 데이터 구축함 <데이터의 한계> 외부 음향데이터가 기존에는 상수도관 내의 이상부분에서의 음향의 차이가 있을 것으로 예측하고 수집하였으나 이상징후의 종류에 따른 차이가 크지 않았음

This dataset includes files from the experiment titled OverhangPartX16 pertaining to a three-dimensional (3D) additive manufacturing (AM) build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung and Brandon Lane on July 3, 2019. Users should refer to the included User Notes file: OverhangX16_In-situData_UserNotes.pdf, which describes unique attributes of this experiment and dataset from previous, similar AMMT datasets. Users should refer to the data description article https://doi.org/10.6028/jres.125.027 for in-depth discussion of the file types and data structure, which are similar for this dataset. The files included in this dataset include the input command files and in-situ process monitoring data. Experiment and measurement metadata may be obtained from the previous dataset: https://doi.org/10.18434/M32233.This data is one of a set of 'AMMT Process Monitoring Datasets', as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project at the National Institute of Standards and Technology (NIST). Ex-situ part characterization data, including X-ray computed tomography measurements, will be provided as they are made available. Readers should refer to the AMMT datasets web page for updates (https://www.nist.gov/el/ammt-temps/datasets).

This dataset includes the files pertaining to a 3D additive manufacturing build performed on the Additive Manufacturing Metrology Testbed (AMMT) by Ho Yeung on July 8, 2018. The files include the input command files and in-situ process monitoring data, and metadata. This data is the first of the AMMT Process Monitoring Reference Datasets (https://www.nist.gov/el/ammt-temps/datasets), as part of the Metrology for Real-Time Monitoring of Additive Manufacturing project (https://www.nist.gov/programs-projects/metrology-real-time-monitoring-additive-manufacturing).Details on the experiment design, data formats and processing, and file structures can be found in the data description article: https://doi.org/10.6028/jres.124.033